Glaciers on the Tibetan Plateau are the main reservoirs of water in the region and much attention is being given to monitor the response of these glaciers to climate variability. Climate forcing varies across the Plateau with the westerlies from inner Eurasia and monsoons from India and East Asia. The surface properties of glaciers characterize the interaction between the atmospheric boundary layer and glaciers. Traditionally, the surface properties of glaciers are observed in situ, but on the Plateau the spatial and temporal variability of such properties cannot be captured by in – situ experiments only. Our main goal is to characterize the surface energy balance of glaciers using a suite of measurements by space – borne observing systems.

Building upon the work done in the previous Dragon investigations, during this 1st Dragon 4 year we focused on the following aspects:

Temporal and spatial variability in the albedo of glaciers and their close surroundings

Monitoring glacier surface flow velocity over an extended period of time

Estimating and mapping the aerodynamic roughness of glaciers

Discriminating ice cover types using observations of spectral reflectance and albedo at high spatial resolution

Relating the albedo of glaciers to depth and duration of snow cover

Parameterizing glacier albedo in the WRF model

Time series of data products on albedo and net radiation were analyzed to assess the variability in space and time of albedo in glacial areas and to document the corresponding impact on net radiation. Snowfall leads to sudden and large increase in albedo, which may be as high as 0.8 with deep, fresh now. This leads to a change in net radiation from about 800 Wm-2 to 200 Wm-2, i.e. an extremely large reduction in radiative load, slowing down snow-melting. The same analysis documented a large spatial variability on both minimum, i.e. glacier, and maximum albedo. Such variability is due to a combination of surficial deposit (ice cover) and terrain, which affects irradiance.

The response of glaciers to climate variability was evaluated in different ways, including the retrieval of the glacier surface velocity using image correlation of paired high spatial resolution images (Landsat TM and OLI). This study focused on the Nyainqêntanglha Range and the period from 1993 to 2015. We analyzed wintertime images at intervals of about one year. The analysis of these time series of ice surface displacement, revealed that the observed signals were a combination of a linear trend and a multi-annual component with variable amplitude from place to place.

Surface features, namely roughness, slope and elevation were retrieved with a combination of ICESat/GLAS and ASTER GDEM data to estimate and map the aerodynamic roughness of glaciers. This property modulates fluxes of latent heat (evaporation, sublimation) and sensible heat. The response of the laser waveform to morphology was studied in detail, showing that roughness and slope of the surface can contribute several meters to even several tens of meters to the pulse shape.

The spatial variability of spectral reflectance and albedo within a glacier was evaluated using Landsat TM and GuoFeng multi –spectral images. Areas covered by debris and lakes or ponds linked with a glacier were clearly delineated, particularly using the Guofeng very high spatial resolution images. Differences in spectral reflectance and albedo were rather large and the consequences in terms of spatial variability, within a glacier, of radiative forcing are being evaluated.

The work on time series of glacier albedo revealed large and rapid variations in the radiative forcing on glaciers. These variations are not reproduced by the land surface schemes currently implemented in advanced atmospheric models such as WRF. We have combined WRF snow depth with MODIS albedo to parameterize the dependence of albedo on snow depth and snow age. This parameterization has been evaluated against in – situ measurements at the Plateau permanent observatories.

The parameterization constructed in this way has been implemented in WRF and the sensitivity of the WRF land surface energy balance has been evaluated, showing rather large impacts on both radiative and convective fluxes.

Oral presentation

Recent advances in the estimation of water losses with ETMonitor driven by satallite dat

One great challenge of cryosphere and hydrosphere science in high elevation regions is the scarcity and sparseness of data on the multiple variables and processes relevant to the understanding of the water cycle.

Quantitative information on water losses is important to understand the global terrestrial water cycle and land – atmosphere interactions. The global evapotrasnspiration in 2008-2013 with a spatial resolution of 1 km was determined using ETMonitor as the sum of the evapotranspiration components, i.e. plant transpiration, soil evaporation, open water evaporation, rainfall interception, snow and ice sublimation. All these variables were retrieved using the ETMonitor model driven by multiple satellite data products. The ASCAT (Advanced Scatterometer) soil moisture data product was applied as a key input to scale ET between 0 and ETmax. To allow the estimation of ET at high spatial resolution, the 0.1° resolution ASCAT data product was downscaled to 1km spatial resolution globally using bilinear resampling method. We have developed a different, bio-phyisical, downscaling procedure applicable for regional studies and based on high resolution surface temperature and vegetation index data products. The estimated water losses agreed well with the in situ tower based observations at a number of FLUXNET sites, with high correlation, low bias, and low root mean square error. The retrieved ET captures the expected global patterns and the details of spatial and temporal patterns were consistent with the current available global evapotranspiration products such as data from GLEAM (Global Land Evaporation Amsterdam Model) and the GLDAS (Global Land Data Assimilation System) Noah product. The ETMonitor data product is superior due to the high spatial and temporal resolutions. We have also experimented with the ESA-CCI (European Space Agency - Climate Change Initiative) soil moisture data product to replace the ASCAT one. A first evaluation was carried out by retrieving ET in China during 2001-2015 and the results used to contribute to the National Remote Sensing Monitoring for Sustanable Devepoment Report in China (2016).

To improve the accuracy of ETMonitor in cold and high elevation regions, the algorithm to estimate snow and ice sublimation was improved, by adapting the Penman – Monteith combination equation. This method was evaluated against eddy – covariance measurements of latent heat flux at high elevation sites in the Heihe river Basin N – W China: on average the RMSE was 8.75 W m-2. We have also evaluated the bulk aerodynamic (BA) method against the same measurements and the BA performance was slightly worse. The main driver of the P – M equation is net radiation, which is very variable at high elevation due to the variability of albedo, which enhances the scope of using satellite observations to estimate and monitor sublimation. A case – study on the upper reach of the Heihe River Basin has been carried out using MODIS data products on surface albedo and temperature.

Work contiuned on improving other algorithms and data products. An algorithm to retrieve the total precipitable water was developed, based on the ratio of brightness temperature changes ΔTb18.7/ ΔTb23.8 , using atmospheric profiles obtained from the globally distributed radiosonde observations and applying a microwave radiative transfer model. This algorithm can retrieve total precipitable water under both clear and cloudy sky condition over land, and can be easily transferred to MWRI on board the FY-3 satellites. To retrieve the surface freeze and thaw condition, an innovative freeze/thaw index based on microwave observations at 18.7 and 36.5 GHz was defined and assumed to be linearly correlated with the radiometric land surface temperature retrieved with thermal infrared observations. It was found that this linear relationship is quite reliable for most areas, and can provide high-resolution information on near surface soil freeze/thaw state. The validation of the high-resolution freeze/thaw state against soil temperature measured at active layer monitoring sites along the Qinghai-Tibet Highway illustrated a moderate accuracy over a decade scale.The daily snow cover fraction at 500m resolution was retrieved in the Tibet Plateau in 2013, and missing values due to the cloud cover were filled to obtain a cloud-free dataset.

Oral presentation

The Potential Application of Microwave Product from Global Precipitation Measurement Mission for Soil Moisture Modeling in Mun River Basin, Thailand

Mun river basin is largest basin in Mekong river region, where both flood and drought frequently occur. Water management by hydrological model is difficult due to lack of accurate and spatial/temporal continuous products to force and localize the model. The new Precipitation and soil microwave products gradually plays important role for hydrological modelling. The study introduced the precipitation product at 0.1 degree spatial resolution (GPM mission) for SM simulation by Variable Infiltration Capacity (VIC) model. Then we assessed accuracy of 10cm soil moisture simulation by comparison with SMAP soil moisture product at a spatial resolution of 9km. The results show that:(1) GPM precipitation can be compared with gauge-based monthly precipitation in Mun river;(2) GPM products can be used to simulate spatial pattern of soil moisture in dry season; the simulated soil moisture has a high accuracy in the upperstream of basin but poor in the downstream due to spatial pattern of irrigation application in Mun river basin. It suggests that based on GPM and SMAP products, we can make model calculation and cross validation by remote sensing technology. Therefore microwave remote sensing products are expected to be introduced for water management in remote or data-lacking regions.

Soil hydrologic parameterization, as well known control the energy and water fluxes of hydrologic basin surface playing a crucial role in hydrological model simulation for operative application in the field of water engineering. Despite their importance their definition for large areas is always a source of uncertainty due to difficulty of representative ground measurements , their spatial variability also strongly affected by land use change and agricultural practices.

In the framework of Dragon 4 Project “Forcing, calibration, validation and data assimilation in basin scale hydrological models using satellite data products”, the paper presents a procedure for soil hydrologic parameterization based on the assimilation of satellite LST data into a thermodynamic distributed water balance model (FEST-EWB). The model algorithm solves the system of energy and mass balances in terms of a representative equilibrium temperature (RET), that is the land surface temperature that closes the energy balance equation and so governs the fluxes of energy and mass over the basin domain. This equilibrium surface temperature, which is a critical model state variable, is comparable to LST as retrieved from operational remote sensing data from MOST, ESA and NASA agencies. This approach will be compared with traditional ones based the pixel wise use of pedo transfer function, calibrated for available soil maps.

The case study is the upper part of the Heihe River basin where a consistent historical data set will allow to test this approach.

Poster

Parameter estimation for a simple two-source evapotranspiration model using Bayesian inference and its application to remotely sensed estimations of latent heat flux at the regional scale

A simple two-source evapotranspiration (ET) model was applied to the Yingke and Daman irrigation districts of the Zhangye Oasis, which is located in the middle reaches of the Heihe River, China. The ET model was composed of two parts, including an evaporation (E) sub-model and a transpiration (T) sub-model. A separated parameter estimation scheme was conducted using Bayesian inference. First, an empirical multiplier was estimated for an E sub-model using observations that were collected after crop harvests. The empirical multiplier was then assigned to the most-likely value in the simple two-source ET model. Second, a global sensitivity analysis was performed to identify the key parameters that were responsible for most of the variability in the λET results within the T sub-model. To avoid equifinality or over-parameterization, Bayesian inference was applied to estimate the key parameters that induced the most variability in the first set. A second set of Bayesian inference was then performed by fixing the most-likely values of these parameters, and the other parameters were defined one-by-one as Bayesian parameters. These parameters were estimated for seven sites. The coefficient of determination for the modeled λET and the observed values exceeded 0.9. Next, a cluster analysis was conducted using the canopy height, leaf area index and soil moisture content to classify the fields with the highest similarities and then to distribute the same parameter values to similar fields. Finally, λET was estimated using the most-likely values of the parameters at the regional scale. The root-mean-square error of the remotely sensed estimates was less than 20 Wm-2, the mean absolute percent error did not exceed 4%, and the correlation coefficient was greater than 0.97. The validation was conducted for both the modeled λET at the point scale and for the remotely sensed λET at the satellite pixel scale. The results demonstrate that using cluster analysis, the most-likely values of the parameters can be effectively applied to estimate remotely sensed λET.

The effect of climate change on snow is complicated at regional scale. The high spatio-temporal resolution snow related variables simulated by weather research and forecast model including snowfall, snow water equivalent and physical snow depth and the high spatial resolution fractional snow cover data extracted from MODIS/Terra are adopted to evaluate the effect of climate change on snow over the Heihe River Basin (HRB) in last 15 years using Empirical Orthogonal Function (EOF) analysis and Mann-Kendall / Theil-Sen trend analysis. The results indicate 1) Due to the air temperature increasing, the fractional snow cover, snow water equivalent, physical snow depth over the whole HRB region decrease in last 15 years, especially at the height over 4500 m, however, the snowfall increases at mid-altitude ranges over the upstream of HRB. 2) Over the upstream of HRB, the total snow flux increased, however, the number of snowfall days decreased in last 15 years, so the occurrence of extreme snow events over the upstream of the HRB might increase. 3) The air temperate over the downstream increased at the most of the HRB, however, the snowfall over this region decreased in last 15 years, so the weak ecological system over the downstream may be exacerbated in future.